Data Analytics: Data analytics is the process of analyzing data sets in order to make the decision about the information they have, increasingly with specialized software and system. Data analytics technologies are used in commercial industries that allow organizations to make business decisions. Data can help businesses better understand their customers, improve their advertising campaigns, personalize their content, and improve their bottom lines. The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics help a business optimize its performance.
Tools: Trifecta, Excel /Spreadsheet, Hive, Polybase, Presto, Trifecta, Excel /Spreadsheet, Clear Analytics.
Role: Data Analysts
->Together Data visualization and analytics will draw conclusions about the datasets. In a few scenarios, it might act as a source for visualization.
sectors: Data Analytics technologies and techniques are widely used in Commercial, Finance, Healthcare, Crime detection, Travel agencies.
![](https://codelido.com/assets/files/2022-12-31/1672488483-86029-image.png)
Data Visualization: Data visualization is the graphical representation of information and data in a pictorial or graphical format(For example: charts, graphs, and maps). Data visualization tools provide an accessible way to see and understand trends, patterns in data and outliers. Data visualization tools and technologies are essential to analyze massive amounts of information and making data-driven decisions. The concept of using pictures is to understand data has been used for centuries. General types of data visualizations are Charts, Tables, Graphs, Maps, Dashboards.
Tools: Plotly, DataHero, Tableau, Dygraphs, QlikView, ZingCHhart
Role: Data Engineers
->Data visualization helps to get a better perception.
sectors: Data Visualization technologies and techniques are widely used in Finance, Banking, Healthcare, Retailing.
key differences:
->Data visualization is the presentation of data in a pictorial or graphical format. Data analytics is also a process that makes it easier to recognize patterns in and derive meaning from, complex data sets.
->Data visualization enables decision makers to see analytics presented visually, so they grasp difficult concepts or identify new patterns.
->Looking at a visualization of an attribute in-depth will lead to the analytics of that attribute.
->The analytics process, including the deployment and use of big data analytics tools, can help companies improve operational efficiency, drive revenue and gain competitive advantages over business rivals.
->Descriptive analytics focuses on describing something that has already happened, as well as suggesting its root causes.
->Prescriptive analytics help companies anticipate business opportunities and make decisions that affect profits in areas such as targeted marketing campaigns etc.
->Predictive analytics help mining historical data sets for patterns indicative of future situations and behaviors
->In visualizations, we have static and interactive visualizations.
->Static visualizations focus on a specific data store, User’s can’t go beyond a single view to explore additional stories beyond what’s in front of them. The story is specifically captured in an engaging single page layout.
->Interactive visualizations help users to select specific data points to build a visualized story of their choosing.
->Data Analytic insight takes discovery to the next level by allowing practitioners to not only explore their data but to understand the underlying factors and impacts beyond simply asking WHY.
->Using charts, graphs, and design elements, data visualization can help business explain trends and stats much more easily. Data visualization also exposes patterns, trends, and correlations that may otherwise go undetected.
->Data analysts translate numbers into plain text (English), whether its sales figures, market research, logistics, or transportation costs.
->Computers made it possible to process large amounts of data at lightning-fast speeds. Today, data visualization has become a rapidly evolving blend of science and art that is certain to change the corporate landscape over the next few years.
->Data analytics is a trending practice that many companies are adopting. Before jumping in and buying data analytics tools, organizations should first get to know the landscape.